// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.

#define TEST_ENABLE_TEMPORARY_TRACKING

#include "main.h"

using namespace std;
template<typename MatrixType>
void
permutationmatrices(const MatrixType& m)
{
	typedef typename MatrixType::Scalar Scalar;
	enum
	{
		Rows = MatrixType::RowsAtCompileTime,
		Cols = MatrixType::ColsAtCompileTime,
		Options = MatrixType::Options
	};
	typedef PermutationMatrix<Rows> LeftPermutationType;
	typedef Transpositions<Rows> LeftTranspositionsType;
	typedef Matrix<int, Rows, 1> LeftPermutationVectorType;
	typedef Map<LeftPermutationType> MapLeftPerm;
	typedef PermutationMatrix<Cols> RightPermutationType;
	typedef Transpositions<Cols> RightTranspositionsType;
	typedef Matrix<int, Cols, 1> RightPermutationVectorType;
	typedef Map<RightPermutationType> MapRightPerm;

	Index rows = m.rows();
	Index cols = m.cols();

	MatrixType m_original = MatrixType::Random(rows, cols);
	LeftPermutationVectorType lv;
	randomPermutationVector(lv, rows);
	LeftPermutationType lp(lv);
	RightPermutationVectorType rv;
	randomPermutationVector(rv, cols);
	RightPermutationType rp(rv);
	LeftTranspositionsType lt(lv);
	RightTranspositionsType rt(rv);
	MatrixType m_permuted = MatrixType::Random(rows, cols);

	VERIFY_EVALUATION_COUNT(m_permuted = lp * m_original * rp, 1); // 1 temp for sub expression "lp * m_original"

	for (int i = 0; i < rows; i++)
		for (int j = 0; j < cols; j++)
			VERIFY_IS_APPROX(m_permuted(lv(i), j), m_original(i, rv(j)));

	Matrix<Scalar, Rows, Rows> lm(lp);
	Matrix<Scalar, Cols, Cols> rm(rp);

	VERIFY_IS_APPROX(m_permuted, lm * m_original * rm);

	m_permuted = m_original;
	VERIFY_EVALUATION_COUNT(m_permuted = lp * m_permuted * rp, 1);
	VERIFY_IS_APPROX(m_permuted, lm * m_original * rm);

	LeftPermutationType lpi;
	lpi = lp.inverse();
	VERIFY_IS_APPROX(lpi * m_permuted, lp.inverse() * m_permuted);

	VERIFY_IS_APPROX(lp.inverse() * m_permuted * rp.inverse(), m_original);
	VERIFY_IS_APPROX(lv.asPermutation().inverse() * m_permuted * rv.asPermutation().inverse(), m_original);
	VERIFY_IS_APPROX(MapLeftPerm(lv.data(), lv.size()).inverse() * m_permuted *
						 MapRightPerm(rv.data(), rv.size()).inverse(),
					 m_original);

	VERIFY((lp * lp.inverse()).toDenseMatrix().isIdentity());
	VERIFY((lv.asPermutation() * lv.asPermutation().inverse()).toDenseMatrix().isIdentity());
	VERIFY(
		(MapLeftPerm(lv.data(), lv.size()) * MapLeftPerm(lv.data(), lv.size()).inverse()).toDenseMatrix().isIdentity());

	LeftPermutationVectorType lv2;
	randomPermutationVector(lv2, rows);
	LeftPermutationType lp2(lv2);
	Matrix<Scalar, Rows, Rows> lm2(lp2);
	VERIFY_IS_APPROX((lp * lp2).toDenseMatrix().template cast<Scalar>(), lm * lm2);
	VERIFY_IS_APPROX((lv.asPermutation() * lv2.asPermutation()).toDenseMatrix().template cast<Scalar>(), lm * lm2);
	VERIFY_IS_APPROX((MapLeftPerm(lv.data(), lv.size()) * MapLeftPerm(lv2.data(), lv2.size()))
						 .toDenseMatrix()
						 .template cast<Scalar>(),
					 lm * lm2);

	LeftPermutationType identityp;
	identityp.setIdentity(rows);
	VERIFY_IS_APPROX(m_original, identityp * m_original);

	// check inplace permutations
	m_permuted = m_original;
	VERIFY_EVALUATION_COUNT(m_permuted.noalias() = lp.inverse() * m_permuted, 1); // 1 temp to allocate the mask
	VERIFY_IS_APPROX(m_permuted, lp.inverse() * m_original);

	m_permuted = m_original;
	VERIFY_EVALUATION_COUNT(m_permuted.noalias() = m_permuted * rp.inverse(), 1); // 1 temp to allocate the mask
	VERIFY_IS_APPROX(m_permuted, m_original * rp.inverse());

	m_permuted = m_original;
	VERIFY_EVALUATION_COUNT(m_permuted.noalias() = lp * m_permuted, 1); // 1 temp to allocate the mask
	VERIFY_IS_APPROX(m_permuted, lp * m_original);

	m_permuted = m_original;
	VERIFY_EVALUATION_COUNT(m_permuted.noalias() = m_permuted * rp, 1); // 1 temp to allocate the mask
	VERIFY_IS_APPROX(m_permuted, m_original * rp);

	if (rows > 1 && cols > 1) {
		lp2 = lp;
		Index i = internal::random<Index>(0, rows - 1);
		Index j;
		do
			j = internal::random<Index>(0, rows - 1);
		while (j == i);
		lp2.applyTranspositionOnTheLeft(i, j);
		lm = lp;
		lm.row(i).swap(lm.row(j));
		VERIFY_IS_APPROX(lm, lp2.toDenseMatrix().template cast<Scalar>());

		RightPermutationType rp2 = rp;
		i = internal::random<Index>(0, cols - 1);
		do
			j = internal::random<Index>(0, cols - 1);
		while (j == i);
		rp2.applyTranspositionOnTheRight(i, j);
		rm = rp;
		rm.col(i).swap(rm.col(j));
		VERIFY_IS_APPROX(rm, rp2.toDenseMatrix().template cast<Scalar>());
	}

	{
		// simple compilation check
		Matrix<Scalar, Cols, Cols> A = rp;
		Matrix<Scalar, Cols, Cols> B = rp.transpose();
		VERIFY_IS_APPROX(A, B.transpose());
	}

	m_permuted = m_original;
	lp = lt;
	rp = rt;
	VERIFY_EVALUATION_COUNT(m_permuted = lt * m_permuted * rt, 1);
	VERIFY_IS_APPROX(m_permuted, lp * m_original * rp.transpose());

	VERIFY_IS_APPROX(lt.inverse() * m_permuted * rt.inverse(), m_original);

	// Check inplace transpositions
	m_permuted = m_original;
	VERIFY_IS_APPROX(m_permuted = lt * m_permuted, lp * m_original);
	m_permuted = m_original;
	VERIFY_IS_APPROX(m_permuted = lt.inverse() * m_permuted, lp.inverse() * m_original);
	m_permuted = m_original;
	VERIFY_IS_APPROX(m_permuted = m_permuted * rt, m_original * rt);
	m_permuted = m_original;
	VERIFY_IS_APPROX(m_permuted = m_permuted * rt.inverse(), m_original * rt.inverse());
}

template<typename T>
void
bug890()
{
	typedef Matrix<T, Dynamic, Dynamic> MatrixType;
	typedef Matrix<T, Dynamic, 1> VectorType;
	typedef Stride<Dynamic, Dynamic> S;
	typedef Map<MatrixType, Aligned, S> MapType;
	typedef PermutationMatrix<Dynamic> Perm;

	VectorType v1(2), v2(2), op(4), rhs(2);
	v1 << 666, 667;
	op << 1, 0, 0, 1;
	rhs << 42, 42;

	Perm P(2);
	P.indices() << 1, 0;

	MapType(v1.data(), 2, 1, S(1, 1)) = P * MapType(rhs.data(), 2, 1, S(1, 1));
	VERIFY_IS_APPROX(v1, (P * rhs).eval());

	MapType(v1.data(), 2, 1, S(1, 1)) = P.inverse() * MapType(rhs.data(), 2, 1, S(1, 1));
	VERIFY_IS_APPROX(v1, (P.inverse() * rhs).eval());
}

EIGEN_DECLARE_TEST(permutationmatrices)
{
	for (int i = 0; i < g_repeat; i++) {
		CALL_SUBTEST_1(permutationmatrices(Matrix<float, 1, 1>()));
		CALL_SUBTEST_2(permutationmatrices(Matrix3f()));
		CALL_SUBTEST_3(permutationmatrices(Matrix<double, 3, 3, RowMajor>()));
		CALL_SUBTEST_4(permutationmatrices(Matrix4d()));
		CALL_SUBTEST_5(permutationmatrices(Matrix<double, 40, 60>()));
		CALL_SUBTEST_6(permutationmatrices(Matrix<double, Dynamic, Dynamic, RowMajor>(
			internal::random<int>(1, EIGEN_TEST_MAX_SIZE), internal::random<int>(1, EIGEN_TEST_MAX_SIZE))));
		CALL_SUBTEST_7(permutationmatrices(
			MatrixXcf(internal::random<int>(1, EIGEN_TEST_MAX_SIZE), internal::random<int>(1, EIGEN_TEST_MAX_SIZE))));
	}
	CALL_SUBTEST_5(bug890<double>());
}
